<![CDATA[This study aims to identify and measure the tendency of public sentiment towards the implementation of the policies of the President of the Republic of Indonesia, Prabowo Subianto. The methodology used is text mining-based sentiment analysis, utilizing a data corpus taken from the social media platform X. This study adopts the SEMMA (Sample, Explore, Modify, Model, Assess) workflow as a procedural framework. Data retrieval is carried out automatically using crawling techniques. Next, the data goes through a comprehensive text pre-processing stage, including cleaning, case folding, normalization, convert negation, tokenizing, stopword removal, stemming. Sentiment polarity is determined automatically through a lexicon-based approach, implemented with the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm. The modeling phase uses two machine learning classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM). Performance testing is carried out on three different training and testing data distribution schemes (90:10, 80:20, and 70:30). The evaluation findings show that the Naïve Bayes algorithm achieved the highest accuracy rate of 81.25% at a ratio of 80:20. Meanwhile, SVM consistently recorded superior accuracy, reaching a maximum value of 92.60% at a ratio of 90:10. Based on a comprehensive assessment of performance metrics (accuracy, precision, recall, and f1-score), the Support Vector Machine (SVM) algorithm was proven to provide significantly superior performance compared to Naïve Bayes in this sentiment classification task]]>
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